Numerical Material Model Coefficient Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Engineers and scientists face difficulties in determining the unknown adjustable coefficients for non-linear material properties in numerical simulations, leading to mismatched physical material test results and excessive data generation, especially since material tests stop at specimen failure.
Innovation Solution
A system and method that involves creating a reference curve from stress-strain data, conducting time-marching simulations with FEA models, using a similarity measure technique to trim and remesh computed curves to match the reference curve, and optimizing coefficients to minimize curve difference measurement parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually guess the unknown coefficients to match physical material test results, then the simulation accuracy may be improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs self-calibration by automatically determining material coefficients through optimization algorithms that minimize the difference between simulated and experimental stress-strain curves, eliminating the need for manual user adjustment and significantly reducing time and effort while maintaining high accuracy
Solution Approach 2:
The system uses feedback from physical material test results to iteratively adjust and optimize the unknown coefficients in the constitutive model, comparing simulated curves with experimental data and refining parameters until optimal match is achieved, thereby improving accuracy without requiring extensive manual intervention
2Quantity of substance
If computation using the formula continues beyond specimen failure, then more data is generated, but it creates excessive computational data that is not physically meaningful
Solution Approach 1:
The system deliberately performs partial computation by stopping the simulation at the point of specimen failure rather than continuing to generate excessive data, using optimization techniques to extract only the meaningful coefficients needed to match the physical test results up to failure, thereby avoiding computational waste while obtaining sufficient data
3Productivity
If an equation or formula is used to calculate material properties, then the execution time is reduced, but the difficulty of determining unknown coefficients increases
Solution Approach 1:
The system automatically determines the unknown coefficients through self-calibration using optimization algorithms that minimize the difference between simulated and experimental curves, eliminating the need for manual user guessing and reducing the complexity of coefficient determination while maintaining fast execution speed
Solution Approach 2:
The system transforms the complex problem of determining multiple unknown coefficients into a parameter optimization problem by defining an objective function that measures the difference between simulated and experimental stress-strain curves, then using numerical optimization to efficiently find the best parameter values
Data Source
AI summary
A reference curve representing measured stress-strain data obtained in a material test of a specimen is received. FEA model is created to represent the specimen which is associated with numerical material properties defined by a formula having a set of adjustable coefficients. Multiple computed curves are obtained each defined with multiple nodes of computed stress-strain values by conducting a time-marching simulation of the material test using the FEA model with a set of unique coefficients. Respective curve difference measurement parameters are calculated between each computed curve and the reference curve using a similarity measure technique that includes trimming off excess end portion of each computed curve so as to match the reference curve. Optimal values of the adjustable coefficients are determined by estimating a minimum of the curve difference measurement parameters according to an optimization technique.